Restoration of a Frontal Illuminated Face Image Based on KPCA
Xiaohua Xie, Wei‐Shi Zheng, Jianhuang Lai, Ching Y. Suen · 2010
In this paper, we propose a novel illumination-normalization method. By using the combination of the Kernel Principal Component Analysis (KPCA) and Pre-image technology, this method can restore the frontal-illuminated face image from a single non-frontal-illuminated face image. In this method, a frontal-illumination subspace is first learned by KPCA. For each input face image, we project its large-scale features, which are affected by illumination variations, onto this subspace to normalize the illumination. Then the frontal-illuminated face image is reconstructed by combining the small- and the normalized large- scale features. Unlike most existing techniques, the proposed method does not require any shape modeling or lighting estimation. As a holistic reconstruction, KPCA+Pre-image technology incurs less local distortion. Compared to directly applying KPCA+Pre-image technology on the original image, our proposed method can be better at processing an image of a face that is outside the training set. Experiments on CMU-PIE and Extended Yale B face databases show that the proposed method outperforms state-of-the-art algorithms.